{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二章 K临近算法\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1 准备到导入数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 编写生产数据函数\n",
    "from numpy import *\n",
    "import operator\n",
    "\n",
    "def createDataSet():\n",
    "    group = array([[1.0,1.1], [1.0,1.0], [0,0], [0,0.1]])\n",
    "    labels = ['A', \"A\", \"B\", \"B\"]\n",
    "    return group, labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  1.1],\n",
       "       [ 1. ,  1. ],\n",
       "       [ 0. ,  0. ],\n",
       "       [ 0. ,  0.1]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group, labels_1 = createDataSet();\n",
    "group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 编写kNN分类算法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#伪代码：\n",
    "#(1) 计算已知类别数据集中的点与当前点的距离\n",
    "#(2) 按照距离递增进行排序\n",
    "#(3) 选取与当前距离最小的k个点\n",
    "#(4) 确定前k个点所在的类别出现的频率\n",
    "#(5) 返回前k个点出现频率最高的类别做为当前点的类别\n",
    "\n",
    "def classify_kNN(inX, dataSet, labels, k):\n",
    "    datasetSize = dataSet.shape[0]\n",
    "    diffMat = tile(inX, (datasetSize, 1)) - dataSet  # 把数据数据重复成dataSet行数，然后相减,得到与每个点的坐标差值\n",
    "    sqDiffMat = diffMat**2                           # 对每个点的差值坐标数据平方\n",
    "    sqDistance = sqDiffMat.sum(axis = 1)\n",
    "    distance= sqDistance**0.5;\n",
    "    sortedDistIndicies = distance.argsort()\n",
    "    \n",
    "    classCount = {}\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1\n",
    "    \n",
    "    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedClassCount[0][0]\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_kNN([0,0], group, labels_1, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fr = open(\"datingTestSet.txt\")\n",
    "arryLines = fr.readlines()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 2.2.1 准备数据：从文本文件中解析数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def file2Matrix(fileName):\n",
    "    fr = open(fileName);\n",
    "    arrayOLines = fr.readlines()\n",
    "    numberOfLnines = len(arrayOLines)\n",
    "    returnMat = zeros((numberOfLnines, 3))\n",
    "    classLabelVector = []\n",
    "    index = 0\n",
    "    \n",
    "    resultIndex = {\"largeDoses\":1, \"smallDoses\":2, \"didntLike\":3}\n",
    "    \n",
    "    for line in arrayOLines:\n",
    "        line = line.strip()\n",
    "        \n",
    "        listFromLine = line.split('\\t')\n",
    "        returnMat[index, :] = listFromLine[0:3]\n",
    "        classLabelVector.append(resultIndex[(listFromLine[-1])])\n",
    "        \n",
    "        index += 1\n",
    "        \n",
    "    return returnMat, classLabelVector;\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'3'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-a669837eb713>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile2Matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"datingTestSet2.txt\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-25-2df28d0c8e09>\u001b[0m in \u001b[0;36mfile2Matrix\u001b[0;34m(fileName)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mlistFromLine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\t'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mreturnMat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlistFromLine\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mclassLabelVector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresultIndex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistFromLine\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '3'"
     ]
    }
   ],
   "source": [
    "x,y = file2Matrix(\"datingTestSet2.txt\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 分析数据：使用matplotlib创建散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eZC+Qhfc61wMBfeZ0Zk3YAsq+W4r+73xLY9/WbF+6h19HrqCJb1ve82/HDx1/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\nxsbGJoL/B0oPQg0XnZkpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5bc84e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7mKHFW+0WbE4bZYXlNGhRj8e/vb9yDCUWkTwOqe4FdQeY6oG5Y8j7EeZmiITn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\nFB8rBmbg7ZoMWu16i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cL8rfQ7pRJVtTxu9M3IkjHIwBrLWJbdhZnfC8puIFaWy0G06uTD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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7b0f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(x[:,1], x[:,0], s=15.0*array(y), c=15.0*array(y))\n",
    "\n",
    "fig2 = plt.figure()\n",
    "a1 = fig2.add_subplot(111)\n",
    "a1.scatter(x[:,1,], x[:,2], s=15.0*array(y), c=15.0*array(y))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 2.2.3 准备数据：归一化数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def autoNorm(dataSet):\n",
    "    minVals = dataSet.min(axis = 0)\n",
    "    maxVals = dataSet.max(0)\n",
    "    ranges = maxVals - minVals\n",
    "    normDataSet = zeros(shape(dataSet))\n",
    "    m = dataSet.shape[0]\n",
    "    normDataSet = dataSet - tile(minVals, (m,1))\n",
    "    normDataSet = normDataSet / tile(maxVals, (m,1))\n",
    "    return normDataSet, ranges, minVals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.44832535,  0.39805139,  0.56195013],\n",
       "       [ 0.15873259,  0.34195467,  0.98657106],\n",
       "       [ 0.28542943,  0.06892523,  0.47417277],\n",
       "       ..., \n",
       "       [ 0.29115949,  0.50910294,  0.51044667],\n",
       "       [ 0.52711097,  0.43665451,  0.4287123 ],\n",
       "       [ 0.47940793,  0.3768091 ,  0.78518234]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normDataSet, ranges, minVals = autoNorm(x)\n",
    "normDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  9.12730000e+04,   2.09193490e+01,   1.69436100e+00])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 2.2.4 测试算法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def datingClassTest():\n",
    "    hoRadio = 0.1\n",
    "    datingDataMat, datingLabels = file2Matrix(\"datingTestSet.txt\")\n",
    "    norMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    m = norMat.shape[0]   #行数\n",
    "    numTestVecs = int(m*hoRadio)  \n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        classifierResult = classify_kNN(norMat[i, :], norMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)\n",
    "        print(\"the classifier came back with: %d, the real anser is: %d\" % (classifierResult, datingLabels[i]))\n",
    "        if (classifierResult != datingLabels[i]):\n",
    "            errorCount += 1\n",
    "        \n",
    "    print(\"the total error rate is %f\" % (errorCount/numTestVecs))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 1\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 1, the real anser is: 1\n",
      "the classifier came back with: 2, the real anser is: 2\n",
      "the classifier came back with: 3, the real anser is: 3\n",
      "the classifier came back with: 1, the real anser is: 3\n",
      "the total error rate is 0.050000\n"
     ]
    }
   ],
   "source": [
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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